Finite Impulse Response Filter for Electroencephalogram Waves Detection

Melinda, Melinda; Syahrial, Syahrial; Yunidar,; Bahri, Al; Irhamsyah, Muhammad

doi:10.53623/gisa.v2i1.65

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…For EEG analysis, FIR filters are preferable over IIR filters because they do not distort waveforms. The FIR filter is used in [21] for detecting the EEG waves.…”

Section: Finite Impulse Response (Fir) Filtermentioning

confidence: 99%

A Survey on EEG Data Analysis Software

Das,

Martin,

Zurales

et al. 2023

Sci

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Electroencephalography (EEG) is a mechanism to understand the brain’s functioning by analyzing brain electrical signals. More recently, it has been more commonly used in studies that are focused on the causation and effect of dementia. More tools are now available to gather EEG data. This brings about the challenge of understanding brain signals, which involves signal processing. Professionals with an electrical engineering background are very comfortable analyzing EEG data. Still, scientists in computer science and related fields need a source that can identify all the tools available and the process of analyzing the data. This paper deals specifically with the existing EEG data analysis tools and the processes involved in analyzing the EEG data using these tools. Furthermore, the paper goes in-depth into identifying the tools and the mechanisms of data processing techniques. In addition, it lists a set of definitions required for a better understanding of EEG data analysis, which can be challenging. The purpose of this paper is to serve as a reference for not only scientists that are new to EEG data analysis but also seasoned scientists that are looking for a specific data component in EEG and can go straight to the section of the paper that deals with the tool that they are using.

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“…For EEG analysis, FIR filters are preferable over IIR filters because they do not distort waveforms. The FIR filter is used in [21] for detecting the EEG waves.…”

Section: Finite Impulse Response (Fir) Filtermentioning

confidence: 99%

A Survey on EEG Data Analysis Software

Das,

Martin,

Zurales

et al. 2023

Sci

View full text Add to dashboard Cite

show abstract

“…Noting the assumptions of previous research studies ( [27], [28], [29], and [30]) we modeled seizures by overlaying random frequencies over a standard EEG signal. Thus, we wrote a python script that added 30 sine waves of frequencies from 8-12 Hz (randomly chosen), with an amplitude of an FIR (Finite Impulse Response) filter of the amplitudes of the standard signal as suggested by [31] and [32]. Phase shifts from 0 to 2π were assigned randomly to encourage constructive and destructive interference, in an attempt to increase the randomness of the generated signal.…”

Section: Seizure Simulationmentioning

confidence: 99%

The Design and Implementation of a Low-Cost Electroencephalogram to Predict Neural Disorders

Anne¹

2022

Preprint

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<p>We designed and developed an EEG to help predict neural disorders. This project achieved both high accuracy and low cost with cost-effective components such as the AD622ANZ instrumentation amplifier and TL048x operational amplifier. Signal processing software and hardware filters were implemented. In addition, a machine learning approach was utilized to develop a binary seizure classifier. Seizure noise was simulated, and future work would revolve around collecting live data from epileptic patients. Future work could show that our design could detect and diagnose other neural disorders. We aim to make this design a closed loop system and BCI (Brain-Computer-Interface) compatible.</p>

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Clinical and neurophysiological effects of bilateral repetitive transcranial magnetic stimulation and EEG-guided neurofeedback in Parkinson’s disease: a randomized, four-arm controlled trial

Romero,

Moreno-Verdú,

Arroyo-Ferrer

et al. 2024

J NeuroEngineering Rehabil

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Background Repetitive Transcranial Magnetic Stimulation (rTMS) and EEG-guided neurofeedback techniques can reduce motor symptoms in Parkinson’s disease (PD). However, the effects of their combination are unknown. Our objective was to determine the immediate and short-term effects on motor and non-motor symptoms, and neurophysiological measures, of rTMS and EEG-guided neurofeedback, alone or combined, compared to no intervention, in people with PD. Methods A randomized, single-blinded controlled trial with 4 arms was conducted. Group A received eight bilateral, high-frequency (10 Hz) rTMS sessions over the Primary Motor Cortices; Group B received eight 30-minute EEG-guided neurofeedback sessions focused on reducing average bilateral alpha and beta bands; Group C received a combination of A and B; Group D did not receive any therapy. The primary outcome measure was the UPDRS-III at post-intervention and two weeks later. Secondary outcomes were functional mobility, limits of stability, depression, health-related quality-of-life and cortical silent periods. Treatment effects were obtained by longitudinal analysis of covariance mixed-effects models. Results Forty people with PD participated (27 males, age = 63 ± 8.26 years, baseline UPDRS-III = 15.63 ± 6.99 points, H&Y = 1–3). Group C showed the largest effect on motor symptoms, health-related quality-of-life and cortical silent periods, followed by Group A and Group B. Negligible differences between Groups A-C and Group D for functional mobility or limits of stability were found. Conclusions The combination of rTMS and EEG-guided neurofeedback diminished overall motor symptoms and increased quality-of-life, but this was not reflected by changes in functional mobility, postural stability or depression levels. Trial registration NCT04017481.

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Finite Impulse Response Filter for Electroencephalogram Waves Detection

Cited by 4 publications

References 10 publications

A Survey on EEG Data Analysis Software

A Survey on EEG Data Analysis Software

The Design and Implementation of a Low-Cost Electroencephalogram to Predict Neural Disorders

Clinical and neurophysiological effects of bilateral repetitive transcranial magnetic stimulation and EEG-guided neurofeedback in Parkinson’s disease: a randomized, four-arm controlled trial

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